---
license: mit
---

# AIDA Cropland delination model
[Github repo](https://github.com/links-ads/aida-cropland-delineation)

## Use cases
it takes a Sentinel-2 image time series as input and predicts a segmentation map of 6 crop classes. It is trained on a Sentinel-2 images of Germany, Lithuania, Latvia, Estonia, Belgium, Austria, Slovenia, Slovakia and Netherlands tagged using [Eurocrops](https://github.com/maja601/EuroCrops)

## Model
The model used is a modified version of [UTAE](https://github.com/VSainteuf/utae-paps), a U-Net like architecture which consists of a convolutional encoder-decoder and central attention module.

### Input
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 10-channel Sentinel-2 images of shape (B x 6 x 10 x 128 x 128), where B is the batch size and the second dimension is the 6 months used for prediction (April to September).

### Preprocessing
The images have to be divided by a factor of 10000 and then clipped in a range of [0, 1].

### Output
The model outputs a segmentation map of the input image.

``int[1, 1, 128, 128]``

## Contributors
* [gaetanochiriaco](https://github.com/gaetanochiriaco) (LINKS Foundation)
* [edornd](https://github.com/edornd) (LINKS Foundation)

## License
MIT License